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Spearman Correlation of Models

Summary of 5_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
10.6 seconds
Metric details
|
score |
threshold |
| logloss |
3.65163 |
nan |
| auc |
0.526683 |
nan |
| f1 |
0.659517 |
7.30509e-96 |
| accuracy |
0.529333 |
0.976911 |
| precision |
0.533333 |
0.999331 |
| recall |
1 |
7.30509e-96 |
| mcc |
0.0564846 |
0.976911 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
3.65163 |
nan |
| auc |
0.526683 |
nan |
| f1 |
0.460245 |
0.976911 |
| accuracy |
0.529333 |
0.976911 |
| precision |
0.52807 |
0.976911 |
| recall |
0.407859 |
0.976911 |
| mcc |
0.0564846 |
0.976911 |
Confusion matrix (at threshold=0.976911)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
493 |
269 |
| Labeled as 1 |
437 |
301 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
0.6 seconds
Metric details
|
score |
threshold |
| logloss |
0.69302 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.659517 |
0.4424 |
| accuracy |
0.492 |
0.4424 |
| precision |
0.492 |
0.4424 |
| recall |
1 |
0.4424 |
| mcc |
0 |
0.4424 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.69302 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.659517 |
0.4424 |
| accuracy |
0.492 |
0.4424 |
| precision |
0.492 |
0.4424 |
| recall |
1 |
0.4424 |
| mcc |
0 |
0.4424 |
Confusion matrix (at threshold=0.4424)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
762 |
| Labeled as 1 |
0 |
738 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 2_DecisionTree |
1 |
| 3_Linear |
2 |
| 4_Default_Xgboost |
1 |
| 6_Default_RandomForest |
2 |
Metric details
|
score |
threshold |
| logloss |
0.685886 |
nan |
| auc |
0.570199 |
nan |
| f1 |
0.659517 |
0.239359 |
| accuracy |
0.564667 |
0.542542 |
| precision |
0.63522 |
0.605214 |
| recall |
1 |
0.239359 |
| mcc |
0.134723 |
0.542542 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.685886 |
nan |
| auc |
0.570199 |
nan |
| f1 |
0.446141 |
0.542542 |
| accuracy |
0.564667 |
0.542542 |
| precision |
0.596372 |
0.542542 |
| recall |
0.356369 |
0.542542 |
| mcc |
0.134723 |
0.542542 |
Confusion matrix (at threshold=0.542542)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
584 |
178 |
| Labeled as 1 |
475 |
263 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
15.2 seconds
Metric details
|
score |
threshold |
| logloss |
0.696578 |
nan |
| auc |
0.526197 |
nan |
| f1 |
0.659517 |
0.360627 |
| accuracy |
0.522667 |
0.436457 |
| precision |
0.510319 |
0.542211 |
| recall |
1 |
0.360627 |
| mcc |
0.0694603 |
0.41876 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.696578 |
nan |
| auc |
0.526197 |
nan |
| f1 |
0.614639 |
0.436457 |
| accuracy |
0.522667 |
0.436457 |
| precision |
0.509821 |
0.436457 |
| recall |
0.773713 |
0.436457 |
| mcc |
0.0611991 |
0.436457 |
Confusion matrix (at threshold=0.436457)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
213 |
549 |
| Labeled as 1 |
167 |
571 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 6_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
29.6 seconds
Metric details
|
score |
threshold |
| logloss |
0.689307 |
nan |
| auc |
0.551346 |
nan |
| f1 |
0.659517 |
0.361855 |
| accuracy |
0.544 |
0.474122 |
| precision |
0.571429 |
0.556599 |
| recall |
1 |
0.361855 |
| mcc |
0.106256 |
0.474122 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.689307 |
nan |
| auc |
0.551346 |
nan |
| f1 |
0.624588 |
0.474122 |
| accuracy |
0.544 |
0.474122 |
| precision |
0.524908 |
0.474122 |
| recall |
0.771003 |
0.474122 |
| mcc |
0.106256 |
0.474122 |
Confusion matrix (at threshold=0.474122)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
247 |
515 |
| Labeled as 1 |
169 |
569 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 4_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
339.4 seconds
Metric details
|
score |
threshold |
| logloss |
0.729976 |
nan |
| auc |
0.561543 |
nan |
| f1 |
0.659517 |
0.0298141 |
| accuracy |
0.555333 |
0.548495 |
| precision |
0.642857 |
0.700356 |
| recall |
1 |
0.0298141 |
| mcc |
0.12643 |
0.700356 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.729976 |
nan |
| auc |
0.561543 |
nan |
| f1 |
0.479313 |
0.548495 |
| accuracy |
0.555333 |
0.548495 |
| precision |
0.565378 |
0.548495 |
| recall |
0.415989 |
0.548495 |
| mcc |
0.110559 |
0.548495 |
Confusion matrix (at threshold=0.548495)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
526 |
236 |
| Labeled as 1 |
431 |
307 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 3_Linear
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Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
18.1 seconds
Metric details
|
score |
threshold |
| logloss |
0.713543 |
nan |
| auc |
0.563638 |
nan |
| f1 |
0.659517 |
0.0869947 |
| accuracy |
0.558 |
0.521119 |
| precision |
0.652542 |
0.733536 |
| recall |
1 |
0.0869947 |
| mcc |
0.114922 |
0.521119 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.713543 |
nan |
| auc |
0.563638 |
nan |
| f1 |
0.525412 |
0.521119 |
| accuracy |
0.558 |
0.521119 |
| precision |
0.556904 |
0.521119 |
| recall |
0.49729 |
0.521119 |
| mcc |
0.114922 |
0.521119 |
Confusion matrix (at threshold=0.521119)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
470 |
292 |
| Labeled as 1 |
371 |
367 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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